Search Results for author: Hao Xu

Found 31 papers, 6 papers with code

Learning Dynamic Preference Structure Embedding From Temporal Networks

no code implementations23 Nov 2021 Tongya Zheng, Zunlei Feng, Yu Wang, Chengchao Shen, Mingli Song, Xingen Wang, Xinyu Wang, Chun Chen, Hao Xu

Our proposed Dynamic Preference Structure (DPS) framework consists of two stages: structure sampling and graph fusion.

Graph Sampling

Whole Heart Anatomical Refinement from CCTA using Extrapolation and Parcellation

no code implementations18 Nov 2021 Hao Xu, Steven A. Niederer, Steven E. Williams, David E. Newby, Michelle C. Williams, Alistair A. Young

In addition to the new labels, the median Dice scores were improved for all the initial 6 labels to be above 95% in the 10-label segmentation, e. g. from 91% to 97% for the left atrium body and from 92% to 96% for the right ventricle.

Predicting the Stereoselectivity of Chemical Transformations by Machine Learning

no code implementations12 Oct 2021 Justin Li, Dakang Zhang, Yifei Wang, Christopher Ye, Hao Xu, Pengyu Hong

Since late 1960s, there have been numerous successes in the exciting new frontier of asymmetric catalysis.

Label Mask for Multi-Label Text Classification

no code implementations18 Jun 2021 Rui Song, Xingbing Chen, Zelong Liu, Haining An, Zhiqi Zhang, Xiaoguang Wang, Hao Xu

In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model.

Classification Language Modelling +1

Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

no code implementations9 Jun 2021 Yuntian Chen, Yingtao Luo, Qiang Liu, Hao Xu, Dongxiao Zhang

Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses.

Robust discovery of partial differential equations in complex situations

no code implementations31 May 2021 Hao Xu, Dongxiao Zhang

In the framework, a preliminary result of potential terms provided by the deep learning-genetic algorithm is added into the loss function of the PINN as physical constraints to improve the accuracy of derivative calculation.

Deep-Learning Discovers Macroscopic Governing Equations for Viscous Gravity Currents from Microscopic Simulation Data

no code implementations31 May 2021 Junsheng Zeng, Hao Xu, Yuntian Chen, Dongxiao Zhang

Although deep-learning has been successfully applied in a variety of science and engineering problems owing to its strong high-dimensional nonlinear mapping capability, it is of limited use in scientific knowledge discovery.

Topological Regularization for Graph Neural Networks Augmentation

no code implementations3 Apr 2021 Rui Song, Fausto Giunchiglia, Ke Zhao, Hao Xu

The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision.

Data Augmentation Unsupervised Representation Learning

OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration

1 code implementation ICCV 2021 Hao Xu, Shuaicheng Liu, Guangfu Wang, Guanghui Liu, Bing Zeng

On the other hand, previous global feature based approaches can utilize the entire point cloud for the registration, however they ignore the negative effect of non-overlapping points when aggregating global features.

Point Cloud Registration

BE-RAN: Blockchain-enabled Open RAN with Decentralized Identity Management and Privacy-Preserving Communication

no code implementations26 Jan 2021 Hao Xu, Lei Zhang, Elaine Sun, Chih-Lin I

In this paper, Blockchain-enabled Radio Access Networks (BE-RAN) is proposed as a novel decentralized RAN architecture to facilitate enhanced security and privacy on identification and authentication.

Cryptography and Security Distributed, Parallel, and Cluster Computing Networking and Internet Architecture

Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data

no code implementations24 Nov 2020 Hao Xu, Dongxiao Zhang, Nanzhe Wang

Our proposed algorithm is also able to discover PDEs with high-order derivatives or heterogeneous parameters accurately with sparse and noisy data.

Multi-Modal Subjective Context Modelling and Recognition

no code implementations19 Nov 2020 Qiang Shen, Stefano Teso, Wanyi Zhang, Hao Xu, Fausto Giunchiglia

Second, existing models typically assume that context is objective, whereas in most applications context is best viewed from the user's perspective.

GripNet: Graph Information Propagation on Supergraph for Heterogeneous Graphs

1 code implementation29 Oct 2020 Hao Xu, Shengqi Sang, Peizhen Bai, Laurence Yang, Haiping Lu

Heterogeneous graph representation learning aims to learn low-dimensional vector representations of different types of entities and relations to empower downstream tasks.

Graph Representation Learning Link Prediction +1

Revisiting Factorizing Aggregated Posterior in Learning Disentangled Representations

no code implementations12 Sep 2020 Ze Cheng, Juncheng Li, Chenxu Wang, Jixuan Gu, Hao Xu, Xinjian Li, Florian Metze

In this paper, we provide a theoretical explanation that low total correlation of sampled representation cannot guarantee low total correlation of the mean representation.

Multistability of Small Reaction Networks

1 code implementation10 Aug 2020 Xiaoxian Tang, Hao Xu

For three typical sets of small reaction networks (networks with two reactions, one irreversible and one reversible reaction, or two reversible-reaction pairs), we completely answer the challenging question: what is the smallest subset of all multistable networks such that any multistable network outside of the subset contains either more species or more reactants than any network in this subset?

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network

1 code implementation5 Jul 2020 Hao Xu, Ka Hei Hui, Chi-Wing Fu, Hao Zhang

To start, we reformulate tiling as a graph problem by modeling candidate tile locations in the target shape as graph nodes and connectivity between tile locations as edges.

Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data

no code implementations16 May 2020 Hao Xu, Dongxiao Zhang, Junsheng Zeng

Next, genetic algorithm is utilized to discover the form of PDEs and corresponding coefficients with an incomplete candidate library.

A Large Scale Speech Sentiment Corpus

no code implementations LREC 2020 Eric Chen, Zhiyun Lu, Hao Xu, Liangliang Cao, Yu Zhang, James Fan

We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium.

Sentiment Analysis

DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm

no code implementations21 Jan 2020 Hao Xu, Haibin Chang, Dongxiao Zhang

In the proposed framework, a deep neural network that is trained with available data of a physical problem is utilized to generate meta-data and calculate derivatives, and the genetic algorithm is then employed to discover the underlying PDE.

D2D-LSTM based Prediction of the D2D Diffusion Path in Mobile Social Networks

no code implementations28 Sep 2019 Hao Xu

However, current mainstream content popularity prediction methods only use the number of downloads and shares or the distribution of user interests, which do not consider important time and geographic location information in mobile social networks, and all of data is from OSN which is not same as MSN.

RTC-VAE: HARNESSING THE PECULIARITY OF TOTAL CORRELATION IN LEARNING DISENTANGLED REPRESENTATIONS

no code implementations25 Sep 2019 Ze Cheng, Juncheng B Li, Chenxu Wang, Jixuan Gu, Hao Xu, Xinjian Li, Florian Metze

In the problem of unsupervised learning of disentangled representations, one of the promising methods is to penalize the total correlation of sampled latent vari-ables.

DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data

no code implementations13 Aug 2019 Hao Xu, Haibin Chang, Dongxiao Zhang

However, prior to achieving this goal, major challenges remain to be resolved, including learning PDE under noisy data and limited discrete data.

Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning

no code implementations2 May 2019 Mohammad Jafari, Vahid Sarfi, Amir Ghasemkhani, Hanif Livani, Lei Yang, Hao Xu

In this paper, a biologically-inspired adaptive intelligent secondary controller is developed for microgrids to tackle system dynamics uncertainties, faults, and/or disturbances.

A Neural-Network-Based Optimal Control of Ultra-Capacitors with System Uncertainties

no code implementations29 Nov 2018 Jiajun Duan, Zhehan Yi, Di Shi, Hao Xu, Zhiwei Wang

Conventional control strategies usually produce large disturbances to buses during charging and discharging (C&D) processes of UCs, which significantly degrades the power quality and system performance, especially under fast C&D modes.

Pixel Chem: A Representation for Predicting Material Properties with Neural Network

no code implementations27 Sep 2018 Shuqian Ye, Yanheng Xu, Jiechun Liang, Hao Xu, Shuhong Cai, Shixin Liu, Xi Zhu

In this work we developed a new representation of the chemical information for the machine learning models, with benefits from both the real space (R-space) and energy space (K-space).

Multiple Instance Curriculum Learning for Weakly Supervised Object Detection

no code implementations25 Nov 2017 Siyang Li, Xiangxin Zhu, Qin Huang, Hao Xu, C. -C. Jay Kuo

When supervising an object detector with weakly labeled data, most existing approaches are prone to trapping in the discriminative object parts, e. g., finding the face of a cat instead of the full body, due to lacking the supervision on the extent of full objects.

Curriculum Learning Multiple Instance Learning +2

Hybrid Affinity Propagation

no code implementations30 Jul 2013 Jingdong Wang, Hao Xu, Xian-Sheng Hua, Shipeng Li

We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images.

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